References & Citations
Computer Science > Computer Vision and Pattern Recognition
Title: Depth-Assisted ResiDualGAN for Cross-Domain Aerial Images Semantic Segmentation
(Submitted on 21 Aug 2022 (v1), last revised 27 Aug 2022 (this version, v2))
Abstract: Unsupervised domain adaptation (UDA) is an approach to minimizing domain gap. Generative methods are common approaches to minimizing the domain gap of aerial images which improves the performance of the downstream tasks, e.g., cross-domain semantic segmentation. For aerial images, the digital surface model (DSM) is usually available in both the source domain and the target domain. Depth information in DSM brings external information to generative models. However, little research utilizes it. In this paper, depth-assisted ResiDualGAN (DRDG) is proposed where depth supervised loss (DSL), and depth cycle consistency loss (DCCL) are used to bring depth information into the generative model. Experimental results show that DRDG reaches state-of-the-art accuracy between generative methods in cross-domain semantic segmentation tasks.
Submission history
From: Zhao Yang [view email][v1] Sun, 21 Aug 2022 06:58:51 GMT (2987kb,D)
[v2] Sat, 27 Aug 2022 08:35:03 GMT (2987kb,D)
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